Magnetoencephalogram (MEG) techniques passively measure the magnetic fields emanating from neuronal sources in the brain. The MEG is the magnetic analogue of the electroencephalogram (EEG), which is a measure of potential differences about the brain caused by the electric fields emanating from neuronal sources within the brain. MEG and EEG signals originate from sources that can be modeled by current dipoles. For example, a hypothetical dendrite of dipole moment Q immersed in the electrically conducting brain is considered. The current dipole causes volume currents to flow in the brain and surrounding tissue, resulting in potential differences at the scalp. Those potentials are the signals measured by the EEG. A magnetic field, B, associated with the current dipole also is generated. This neuromagnetic field, which is measured by the MEG, has a field-spatial pattern giving contours of constant field (plus contours for B exiting, minus contours for B entering). The magnitude of the field diminishes as the contour radius increases.
The MEG has theoretical advantages over the EEG. A magnetic field noted as a vector B gives directional information about the source orientation. The neuromagnetic field B is not distorted by the brain, since the brain has the same permeability as air. The MEG is an absolute measure of source strength, not measured with respect to a reference, as is the EEG. The MEG is not affected by bad electrode contact or tissue artifacts, as is the EEG. The MEG does not need to touch the head, as does, the EEG.
The MEG has led to several investigations for locating the neuronal sources of magnetic signals emanating from the Brain under normal or pathological conditions. Neuromagnetic signals seen under normal conditions might be evoked responses from visual, auditory, somatic, or other stimuli. Normal brain rhythms (e.g., alpha and beta) are also seen. Neuromagnetic signals observed under pathological conditions might be associated with epilepsy4 or some other disease. The MEG shows potential for non-invasively localizing some sources of epilepsy located deep in the brain. At present, such sources are often localized by using EEG electrodes penetrating the brain. The MEG also shows potential for straightforward functional imaging of brain activity in mind-brain investigations. For example, psycho-physiological tests have shown that the MEG can measure brain activity during motor action and also in anticipation of motor action and in the absence of motor action. Such a dynamic real-time imaging response is not readily attainable by expensive positron emission tomography or magnetic resonance imaging systems.
FIGS. 1A-B illustrate example loops used in conventional single-axis gradiometer of conventional MEG systems.
In one example, a loop receives a neuromagnetic signal and noise. The loop converts received neuromagnetic signal plus noise to an electrical representation. This will be described with reference to FIG. 1A.
FIG. 1A is an illustration for a example conventional loop 102.
Loop 102 includes an entry portion 110, an exit portion 112 and a loop portion 114.
A detected magnetic field 116 passes through loop 102 and an electrical current 118 is generated. Electrical current 118 travels in the direction of an arrow 119. The amount of current generated is associated with an the amount of magnetic field received for detected magnetic field 116. Detected magnetic field 116 includes an amount of signal in additional to an amount of noise. In the case of use in a MEG system, the noise can be a factor of 10,000 times larger than the neuromagnetic signal. Generally, the noise originates from sources distant from the biological signal, and the noise field is spatially uniform. Loop 102 will detect a magnetic field along a single axis. When used in as a gradiometer, the gradiometer may detect a magnetic gradient along a single axis—the axis through the center of loop 102.
Methods have been developed to remove noise in a detected signal. For example, a first loop may be used to receive a neuromagnetic signal plus noise and another loop may be used to receive only noise. A combination may be used to remove the noise and generate a noise-free neuromagnetic signal. This will be further described with reference to FIG. 1B.
FIG. 1B is an illustration for another example conventional loop 104.
Loop 104 includes loop 102 and a loop 120.
Loop 120 detects a noise field 122. Loop 120, usually spaced several centimeters from loop 102, is used to measure the noise and not the neuromagnetic biological signal. Detected noise field 122 generates an electrical current 123. Electrical current 123 travels in the direction of an arrow 124. Loop 120 includes an input 125 and an output 126. The locations for the inputs and outputs are reversed for loop 102 as compared to loop 120. Electrical current 123 is in the opposite direction of electrical current 118 resulting in a subtraction of the currents. The subtraction in the currents results in noise being subtracted from the combination (of signal and noise) leaving a noise-free signal. This first-order difference subtracts out the spatially uniform noise field and reveals the signal. That difference can be accomplished by using one wire and winding the loops in opposing directions, as shown. This one-difference device is known as a single-differencing (or first-order) gradiometer. Similar to loop 102 discussed above, loop 104 will detect a magnetic field along a single axis. When used in as a gradiometer, the gradiometer may detect a magnetic gradient along a single axis—the axis through the center of loop 104.
In another example, a plurality of loops may be used to filter a non-spatially uniform noise field that varies linearly with distance. This will be further described with reference to FIG. 1C.
FIG. 1C is an illustration for a example conventional loop 106.
Loop 106 includes loop 102, loop 120, a loop 127 and a loop 128. Loop 127 and loop 128 are located between loop 102 and loop 120. Loop 127 and loop 128 form a difference of a difference (i.e., double difference) to cancel the noise. Such a device is called a double-differencing (or second-order) gradiometer. This configuration may be used for a non-spatially uniform noise field which varies linearly with distance. Similar to loop 102 discussed above, loop 106 will detect a magnetic field along a single axis. When used in as a gradiometer, the gradiometer may detect a magnetic gradient along a single axis—the axis through the center of loop 106.
FIG. 1D is an illustration for an example conventional loop 108.
Loop 108 includes loop 102, loop 120, a multi-turn loop 129 and a multi-turn loop 130.
Multi-turn loop 129 and multi-turn loop 130 are located between loop 102 and loop 120 with multi-turn loop 129 located adjacent to loop 102 and multi-turn loop 130 located adjacent to loop 120. Similar to loop 102 discussed above, loop 108 will detect a magnetic field along a single axis. When used in as a gradiometer, the gradiometer may detect a magnetic gradient along a single axis—the axis through the center of loop 108.
For conventional versions of this configuration, gradiometer magnetometers can have multi-turn loops immersed in a cryogenic fluid (e.g., liquid helium) to allow the loops to become superconducting. Superconducting loops are connected to a superconducting quantum interference device (SQUID), which is usually another wire loop with a Josephson tunneling junction used to detect the current flowing in the pick-up loop. The Josephson effect is the phenomenon of super current (i.e. a current that flows indefinitely long without any voltage applied) across to superconductors coupled by a weak link.
The pickup loop may be used in an MEG system, wherein liquid helium is used for cooling components. This will be described in greater detail with reference to FIG. 2A.
FIG. 2A is an illustration for a conventional MEG system having a conventional gradiometer 200 using the second-differencing pickup loop 106 as described with reference to FIG. 1C.
Conventional second-differencing gradiometer 200 includes loop 106, a SQUID 202, a liquid helium portion 204 and a housing 206.
SQUID 202 is used to measure weak magnetic fields using Josephson junction devices. A Josephson junction device relies on the Josephson phenomenon of a direct current crossing from the insulator in the absence of any external electromagnetic field, owing to tunneling. This DC Josephson current is proportional to the sine of the phase difference across the insulator. Liquid helium portion 204 is used to cool loop 106 and SQUID 202. Housing 206 is used for enclosing loop 106, SQUID 202 and liquid helium portion 204.
A single-field point measurement is made with the Second-Differencing Gradiometer (SSDG) at one station. As discussed above, SQUID 202 will detect a magnetic field along a single axis—the axis through the center of loop 106. To locate a single magnetic field, based on a motion of a magnetic dipole within the head, SQUID 202 will need to detect magnetic fields along many axes. Therefore, conventions MEG systems have as many as 100 gradiometer channels statically positioned about the head. The results from the as many as 100 measurements are then used in signal processing such as a Radon transform. An inverse the Radon transform is then used reconstruct an original magnetic field map of the head to deduce a location of a motion of a magnetic dipole within the head.
The MEG measurement is difficult. Non-limiting examples of background magnetic noise competing with neuromagnetic signals includes geomagnetic, geologic, urban, seismic, biological and sensor. Although gradiometric implementations can reduce the effects of some noise sources, the best MEG measurements also require the use of a magnetically shielded room, seismic isolation, auxiliary sensors and adaptive signal processing for further reduction of noise.
Conventional gradiometer 200 has issues with safety and volatility as super-cooled liquids such as Helium are used to provide cooling. Additionally, significant pressures are used for conventional gradiometer 200 requiring high levels of maintenance and monitoring. Furthermore, conventional gradiometer 200 are extremely large and complex and as a result are configured for stationary operation. Additionally, conventional gradiometer 200 consumes large amounts of energy and is very expensive to acquire and operate. Liquid Helium itself can be a scarce resource.
FIG. 2B is a picture of a conventional gradiometer system 210.
Gradiometer system 210 includes a plethora of loop 106 as described with reference to FIG. 2A.
FIG. 2B is a picture of a conventional gradiometer system demonstrating the size and complexity of a conventional gradiometer system.
As mentioned above, a MEG system may be used to map magnetic field contours on the head of a person. This will be further described with reference to FIGS. 3A-3D.
FIGS. 3A-D illustrate magnetic field contours derived with conventional systems.
FIG. 3A illustrates the magnetic field map over which an SSDG performs measurements. In the figure, a head 302 has an actual magnetic field contour map 304.
FIG. 3B illustrates the magnetic field data as measured for the magnetic field map as described with reference to FIG. 3A.
As shown in FIG. 3B, a magnetic field portion 306 is received via conventional second-differencing gradiometer 200, as described with reference to FIG. 2. Here, a first signal 308 and a second signal 310 are detected.
FIG. 3C illustrates the magnetic field data as measured for the magnetic field map at 1 second per position. In this example, four hours are needed to complete the measurement sequence. Here, first signal 308 and second signal 310 lead to subtle changes in the contour map as illustrated by a change 312.
FIG. 3D illustrates the magnetic field data as measured for the magnetic field map at 1 second per position with four hours needed to complete the measurement sequence.
First signal 308 and second signal 310 lead to subtle changed in contour map as illustrated by a change 314.
A contour map is drawn from the data, and sometimes, depending on the algorithm used, the dipole location is found along a line drawn to connect the positive peak contour with the negative peak contour. Generally, these simultaneous measurements are averaged over several seconds that cannot resolve the electrical track of the current dipoles as they progress through the brain to the region of the brain where the electrical activity is climaxed (e.g., cortex, hippocampus).
FIG. 4A illustrates a chart 400 for in vitro measurements performed using a conventional SSDG and spherical container filled with saline solution.
Chart 400 includes an x-axis 402 with units of degrees and a y-axis 404 representing magnetic field with normalized units.
The SSDG measures the magnetic field at different angular positions around oscillating current dipoles located 0.5, 1.5, 2.5, and 3.5 cm away from the center of a sphere (not shown) filled with saline solution. In one example, the sphere may be a 500-ml spherical flask.
A triangle dotted line 406 represents an oscillating current dipole located 1.0 cm away from the center of the container. A circle dotted line 408 represents an oscillating current dipole located 1.5 cm away from the center of the container. A heart dotted line 410 represents an oscillating current dipole located 2.5 cm away from the center of the container. A box dotted line 412 represents an oscillating current dipole located 3.5 cm away from the center of the container.
Triangle dotted line 406 initiates at an approximate y-axis value of 0.4 at an x-axis value of 0 degrees and increases monotonically until it reaches approximately a y-axis value of 3.5 and an approximate x-axis value of 70 degrees. Triangle dotted line 406 decreases monotonically from approximate y-axis value of 3.5 and an approximate x-value of 70 degrees until it reaches an approximate y-axis value of 2.5 and an x-axis value of 120 degrees.
Circle dotted line 408 initiates at an approximate y-axis value of 0.2 at an x-axis value of 0 degrees and increases monotonically until it reaches approximately a y-axis value of 3.4 and an x-axis value of 62 degrees. Circle dotted line 408 decreases monotonically from approximate y-axis value of 3.4 and an approximate x-axis value of 62 degrees until it reaches an approximate y-axis value of 1.7 and an x-axis value of 120 degrees.
Heart dotted line 410 initiates at an approximate y-axis value of 0.4 at an x-axis value of 0 degrees and increases monotonically until it reaches approximately a y-axis value of 3.4 and an x-axis value of 40 degrees. Heart dotted line 410 decreases monotonically from approximate y-axis value of 3.4 and an approximate x-axis value of 40 degrees until it reaches an approximate y-axis value of 1.1 and an x-axis value of 120 degrees.
Box dotted line 412 initiates at an approximate y-axis value of 0.1 at an x-axis value of 0 degrees and increases monotonically until it reaches approximately a y-axis value of 3.4 at an x-axis value of 33 degrees. Box dotted line 412 decreases monotonically from approximate y-axis value of 3.4 and an approximate x-axis value of 33 degrees until it reaches an approximate y-axis value of 0.6 and an x-axis value of 120 degrees.
The measurements show well-defined peaks that fit well with theory, provided the signal to noise ratio is large enough. Further, in this example, the container is spherical, thus the measurements are based on a somewhat symmetrical body. However, a human head is not spherical. To emulate a human head, a non-spherical shape should be used.
FIG. 4B illustrates a chart for measurements similar to FIG. 4A, however in FIG. 4B, the measurements were performed using a conventional SSDG and non-spherical container filled with saline solution. In particular, FIG. 4B illustrates the empirical results of similar measurements as FIG. 4A on oscillating current dipoles located 0.5, 1.0, 1.5, 2.5, and 3.5 cm away from the center of a non-spherical, teardrop-shaped reservoir filled with saline solution.
A diamond dotted line 414 represents an oscillating current dipole located 0.5 cm away from the center of the container.
Triangle dotted line 406 initiates at an approximate y-axis value of 0.3 at an x-axis value of 0 degrees and increases until it reaches an approximate y-axis value of 3.5 and an x-axis value of 120 degrees.
Circle dotted line 408 initiates at an approximate y-axis value of 0.3 at an x-axis value of 0 degrees and increases until it reaches an approximate y-axis value of 3.5 and an approximate x-axis value of 55 degrees. Circle dotted line 408 decreases from an approximate y-axis value of 3.5 and an approximate x-axis value of 55 degrees until it reaches an approximate y-axis value of 2.5 at an x-axis value of 120 degrees.
Heart dotted line 410 initiates at an approximate y-axis value of 0.3 at an x-axis value of 0 degrees and increases until it reaches approximately a y-axis value of 3.5 and an x-axis value of 40 degrees. Heart dotted line 410 decreases monotonically from an approximate y-axis value of 3.5 and an x-axis value of 40 degrees until it reaches an approximate y-axis value of 1.4 and an x-axis value of 120 degrees.
Box dotted line 412 initiates at an approximate y-axis value of 0.1 at an x-axis value of 0 degrees and increases monotonically until it reaches approximately a y-axis value of 3.5 and an x-axis value of 33 degrees. Box dotted line 412 decreases monotonically from approximate y-axis value of 3.5 and an x-axis value of 33 degrees until it reaches an approximate y-axis value of 0.7 and an x-axis value of 120 degrees.
Diamond dotted line 414 initiates at an approximate y-axis value of 0.2 at an x-axis value of 0 degrees and increases until it reaches a y-axis value of approximately 3.2 at an x-axis value of 120 degrees.
There is virtually no peak in the magnetic field pattern for in depth sources, and localization using the spherical theory is not possible. Investigators often fail to quantify the signal to noise ratio, laboratory noise, and the dependence of their localization on the number of measurement points and the choice of localization algorithm.
FIG. 4B illustrates a chart for measurements similar to FIG. 4A performed using a conventional SSDG and non-spherical container filled with saline solution where theory and practice may diverge.
FIG. 5 illustrates a chart 500 for Monte Carlo simulations of 10,000 trials of the localization performance amidst laboratory Gaussian noise using a conventional SSDG. Monte Carlo methods are a class of computational algorithms that rely on repeated random sampling to compute their results. These methods are most suited to calculation by computer and tend to be used when it is infeasible to compute an exact result with a deterministic algorithm.
The probability of localization measurement error for a dipole in a sphere with radius of 10 cm is described in terms of the signal to noise ration of the measurement. The dipole is positioned 4 cm from the sphere center.
Chart 500 includes an x-axis 502 representing a measurement error in millimeters and a y-axis 504 representing cumulative probability with units of normalized percent. A line 506 represents a signal to noise ratio of 10 dB. A line 508 represents a signal to noise ratio of 15 dB. A line 510 represents a signal to noise ratio of 20 dB. A line 512 represents a signal to noise ratio of 25 dB. The probability for error increases as the dipole is positioned closer to the sphere center.
The probability of reduced localization error is highly dependent on the signal to noise ratio. In simple MEG systems with a single channel, moving SSDG MEG devices from station to station causes three major problems. First, as a result of their extreme sensitivity and vector measurement potential, they can vibrate and give false signals resulting from their motion in the earth's static magnetic field. At the various measurement stations, the experimental vibration spectrum is different. Second, MEG devices are cryogenically cooled with liquid helium (i.e., they are superconducting magnetometers). Movement of those sensors from station to station around the head to generate contour maps causes inaccuracies in calibration due to the tilt and changes in helium levels. Third, at the new station or measurement point, the balance between the static magnetic field and internal magnetic trim tabs changes, creating potential calibration error.
FIG. 5 illustrates a chart for Monte Carlo simulations of 10,000 trials of the localization performance amidst laboratory Gaussian noise using a conventional SSDG where the probability for error increases as the dipole is positioned closer to the sphere center.
What is needed is a portable system and method for performing MEG.